http://www.diva-portal.org
This is the published version of a paper published in BMC Public Health.
Citation for the original published paper (version of record):
Mostafavi, F., Piroozi, B., Mosquera, P., Majdzadeh, R., Moradi, G. (2020)
Assessing horizontal equity in health care utilization in Iran: a decomposition analysis
BMC Public Health, 20: 914https://doi.org/10.1186/s12889-020-09071-z
Access to the published version may require subscription.
N.B. When citing this work, cite the original published paper.
Permanent link to this version:
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173502
R E S E A R C H A R T I C L E Open Access
Assessing horizontal equity in health care utilization in Iran: a decomposition analysis
Farideh Mostafavi1, Bakhtiar Piroozi1, Paola Mosquera2, Reza Majdzadeh3and Ghobad Moradi1,4*
Abstract
Background: Despite the goal of horizontal equity in Iran, little is known about it. This study aimed i) to assess socioeconomic inequality and horizontal inequity in the healthcare utilization; and ii) to explore the contribution of need and non-need variables to the observed inequalities.
Methods: This study used national cross sectional dataset from Utilization of Health Services survey in 2015.
Concentration Index (C), Concentration Curve (CC) and Horizontal Inequity index (HI) were calculated to measure inequality in inpatient and outpatient health care utilization. Decomposition analysis was used to determine the contribution of need and non-need factors to the observed inequalities.
Result: Results showed the pro-poor inpatient services in both rural (C =− 0.079) and non-rural areas (C = − 0.096) and the pro-rich outpatient services in both rural (C = 0.038) and non-rural (C = 0.007). After controlling for need factors, HI was positive and significant for outpatient services in rural (HI = 0.039) and non-rural (HI = 0.008), indicating that for given need, the better off especially in rural make greater use of outpatient services. The HI was pro-poor for inpatient services in both rural (HI =− 0.068) and non-rural (HI = -0.090), was significant only in non- rural area. Non-need factors were the most important contributors to explain inequalities in the decomposition analysis.
Conclusion: Disentangle the different contribution of determinants, as well as greater HI in rural areas for
outpatient and in non-rural areas for inpatient services, provide helpful information for decision makers to re-design policy and re-distribute resource allocation in order to reduce the socioeconomic gradient in health care utilization.
Keywords: Health inequalities, SOCIO-ECONOMIC, SOCIAL INEQUALITIES, HEALTH SERVICES
Background
Equitable access and utilization of health services is one of the goals, tasks and challenges of governments [1].
Universal health coverage (UHC) is an important step toward achieving equity in the utilization of health ser- vices by all people [2, 3]. Typically, in high-income countries poorer individuals utilize more health care ser- vices due to need factors (i.e. lower health status).
Conversely in low-income countries, poorer individuals are less likely to use services due to non-need factors (i.e. low income and lack of health insurance) and des- pite their greater need [4]. The principle of Universal health coverage (UHC) states that individuals with equal needs should utilize equal healthcare services [5, 6].
Therefore, as poorer individuals often face lower health status and greater need it is expected that they utilize more health services. but also to support the fullfilment of the UHC Monitoring horizontal equity is deemed necesary not only to provide a comprehensive picture of equity in health care [7,8].
Iran, like many other countries, has set UHC and health equity as some of its main goals [9]. One of the
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence:[email protected]
1Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
4Department of Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Pasdaran Ave, Sanandaj, Iran
Full list of author information is available at the end of the article
first effort was the establishment of a public health care (PHC) network fully financed by the government in 1985 [10–12]. In 1989 The Social Security Act was enacted and the Social Security Organization was appointed as the institution responsible to implement and provide health care services for workers and persons covered by the Labor and Social Security Law. In addition, the Imam Khomeini Relief Foundation, was created by the government as a subsidy fund to cover in- patient services for poor and low-income individuals [10]. In 2005 the Family Physician Program and a Uni- versal Health Insurance scheme were implemented with full financial support for rural areas, and partial financial support for urban residents [12,13]. Lately, in 2014 the implementation of the Health Transformation Plan was an important step taken by the government (more focus on inpatient services in public sector) to achieve public health coverage through reducing the amount of out of pocket payment [14–16].
Little is known about equality in health care utilization in Iran, and equity was not studied nationally or meth- odologically. Previous studies in Iran have shown pro- rich inequalities in healthcare utilization, in which Sex, place of residence and health insurance coverage have been reported as the main predictors of observed in- equalities [17–19]. On the same data of this study, a study using logistic regression models to analyze associ- ation between social variables with the self-reported need and usage of services in people who reported need;
poor people reported more both of outpatient and in- patient needs than rich people, as well as usage of in- patient services was more in rich people and was not significant for outpatient services [20].
Equity in health care in Iran may therefore require be- ing re-assessed and permanently monitored. To contrib- ute and update the knowledge about horizontal equity in health care utilization in Iran, the present study aimed:
i) to assess socioeconomic inequality and horizontal in- equity in the utilization of health services; and ii) to ex- plore the contribution of need and non-need variables to the observed inequalities.
Methods
Study design and participants
This study used national cross sectional dataset from Utilization of Health Services (UHS) survey in 2015.
UHS was conducted by National Institute of Health Re- search under the supervision of the Statistical Centre of Iran and in coordination with the relevant departments in the Ministry of Health and Medical Education.
The target population of this study was a set of ordin- ary resident households (ordinary households are made up of several people who live together in a fixed resi- dence, have the same expenditure and usually eat
together) and group households, i.e. a group of people who all or most of them, due to their special circum- stances, mainly have a common feature, have chosen a joint residence for their living and jointly manage the af- fairs of life in that residence. These were selected ac- cording to the latest general population and housing census of Iran in 2011. Institutional households such as student dormitories, barracks, and prisons were not in- cluded in the study.
The samples were selected using three-stage stratified probability sampling method; i) each province was classi- fied into non-rural/rural geographic segment. ii) the non-rural areas were classified into two categories of
“central city/non-central city” segment. iii) 20 house- holds were selected from each segment using simple random sampling method. Which 10 households were selected as the main sample and 10 households were se- lected as the alternative sample.
The total number of segments in the whole country (m) is obtained by dividing the number of ordinary and group households by 10, and sample areas obtained from following formula:
mth¼ ffiffiffiffiffiffiffiffi Nth
p P ffiffiffiffiffiffiffiffi
Nth
p m t ¼ 1; 2; 3; …; 31 ; h
¼ 1; 2; 3 ð1Þ
mthis the number of sample areas in the hthclass of tth province.Nth is the number of ordinary resident house- holds living in the hthclass/category of the tthprovinces (from 31 provinces) based on the general census of population and housing in 2011.
A total of 22,470 households were enrolled in this study (N = 81,137 invited, N = 78,378 participated), and the response rate was 96.6%.
For the present study, resulting in a sample size of 12, 944 individuals had been received health care from health care facility in the last 2 weeks, and 5404 individ- uals had been admitted to a hospital in the last year.
Data were collected using a questionnaire via interviews.
This study was conducted according to the guidelines laid down in the Declaration of Helsinki.
Variable definition
Outcome variables were measured by health care utilization in inpatient and outpatient care, derived from the questions: “Have you been admitted to a hospital in the last year?” and “Have you received health care from a health care facility in the last two weeks?” respectively.
Both variable coded as yes = 1 or no = 0.
To calculate the socioeconomic status variable, we used the data on a number of assets collected as part of the UHS survey. Using principal component analysis (PCA), an asset index was calculated for each of the
Mostafaviet al. BMC Public Health (2020) 20:914 Page 2 of 9
subjects. We divided individuals based on“rank” instead of “weight” for the quintiles included in the decompos- ition. The index ranks people from the poorest to the richest, by classifying them into five quintiles: very poor, poor, moderate, rich, and very rich [21,22].
Need factors included demographic variables (age and sex) and a health variable (self-reported health), used as proxies of need [4]. Age was categorized into three groups, less than 30 years, 30–59 and 60 years and older.
Sex was defined as male/female. Self-reported health sta- tus variable was dichotomized into two groups good and poor. The information for this variable was derived from the question: “you had Have any major illness or suf- fered from any disability for at least the past year?” (Yes/
No). Having either illness or disability was considered poor health and having no illness or disability was mea- sured as good health status.
Non-need factors included socioeconomic status vari- ables, education, basic and supplementary insurance, marital status, and occupation. Education was catego- rized into three groups: uneducated & elementary, mid- dle & high school and college and above. Basic and supplementary insurance variables were both coded as yes = 1 or no = 0. Marital status was categorized into married or single and occupation or having job was de- fined as yes/no.
Statistical analysis
First Concentration Index (C), Concentration Curve (CC) and Horizontal Inequity index (HI) were calculated to measure inequality in health care utilization. To form CC, individuals are sorted according to their socioeco- nomic status, then the cumulative percentage of popula- tion is plotted against the cumulative percentage of health variable. CC above (below) the line of equality in- dicate health variable is concentrated among poor (rich) individuals. C values range from + 1 to − 1. Positive (negative) value indicates the health variable is concen- trated among rich (poor) individuals, and C equals zero means there is no inequality. To calculate the C, The Kak- wani method was used by the following eq. (22): (Eq.2)
C ¼2
μ cov yð i; RiÞ ð2Þ
Where C is concentration index, Cov is the Covari- ance, yi is the health variable, Ri is the ith individual’s fractional rank in the socioeconomic distribution and μ is the health variable mean. Wagstaff correction [23] to the C was used because of binary outcome variables.
Decomposition analysis was used to determine the contribution of need and non-need factors to the
observed inequalitc we used the linear approximation of a probit model to estimate partial effects [22] (Eq.3).
yi¼ αmþX
jβmjxjiþX
kγmkZkiþ εi ð3Þ
The decomposition of the concentration index for yi, can thus be expressed as the following formula (Eq.4):
C ¼X βmjxj
μ
CjþX γmkZk
μ
CkþGCε
μ ð4Þ
where, μ is the mean yi (health care utilization), Cjand Ck are the concentration indices for Xj (need factors) and Zk (non-need factors), βmj and γmk are the partial effects (dy/dxj, dy/dzk) for x and z, xj and Zk are the mean level of Xjand Zk, ðβmjxj
μ ÞCj and ðβmjxj
μ ÞCj are the contributions of need variables (j) and non-need vari- ables (k), and GCμε is the generalized concentration index for the remaining error [22,24].
Finally, the horizontal index was obtained from the concentration index presented by Eq. (1) minus the esti- mated contributions of the need variables calculated in Eq. (4). When the horizontal index (HI) is positive, the use of services by individuals with a higher socioeco- nomic status is more than their need, and when it is negative it indicates that the poor people of the commu- nity have received services more than their need [25].
The reference groups employed in the analysis were sin- gle, women, under the age of 30, who had college and above education, were in the highest socioeconomic quintile and had basic and complementary insurance.
All analyses were performed on rural and non-rural separately.
Results
Table 1 shows the characteristics of the participants.
The C for inpatient services were negative in both rural (C = -0.079) and non-rural areas (C = -0.096) pointing to- ward a higher utilization among individuals belonging to lower income households. On the other hand, the C for outpatient services were positive in both rural (C = 0.038) and non-rural (C = 0.007), which indicated concentration of these services in higher socioeconomic groups. The concentration curves in Fig. 1, confirmed what was indicated by the Concentration indexes.
After controlling for need factors, the HI was positive and significant for outpatient services in rural (HI = 0.039) and non-rural areas (HI = 0.008), indicating that for given need, the better off make greater use of outpatient services. The HI remained negative for in- patient services in rural (HI = -0.068) and non-rural areas (HI = -0.090), which was significant only in non-
rural area indicating that the inpatient services were more utilized by the poor groups (Table2).
Table 2 (inpatient services) and Table 3 (outpatient services) show the results of the decomposition analysis by non-rural and rural areas. The first column, regres- sion coefficients show the partial effect of each variable
on the utilization. The second column indicates the elas- ticity of health care utilization for each determinant. The third column shows the concentration index of each of the determinants included in the analysis. The two last columns show the absolute and percentage contributions of each factors to the overall concentration index.
Regarding the utilization of inpatient care, there was a significant positive association between utilization of ser- vices and older age, poor health and low SES in rural residents. In non-rural area, being married, unemployed and individuals who reported poor health, were more likely to use services. Male, people in middle age (30–
59) and poor health status were all concentrated among the poor individuals. Need factors explained a smaller proportion of the inequality favoring the poor in both rural and non-rural areas (13.83 and 5.84% respectively), while non-need factor accounted for bigger proportion of the inequality (57.024 and 39.83% in rural and non- rural areas, respectively). Among the need factors, poor health was the major contributor, whereas the other fac- tors displayed an insubstantial role. Among the non- need factors, lack of supplementary insurance and low SES made the largest contributions to explain the pro- poor inequalities in both rural and non-rural areas.
For the utilization of outpatient care, in rural areas, there was a negative association between older age, sex, marital status and lack of basic insurance and a positive association between high SES, poor health, being un- employed and lack supplementary insurance with use of this service. On the other hand, in non-rural areas, indi- viduals with poor health, high SES and lacking supple- mentary insurance were more likely to use the services.
The need factors were slightly offsetting (− 2.26% and − 16.30% in rural and non-rural, respectively) the contri- bution of non-need factors which in this case accounted for most of the inequality favoring the better-off (92.96 and 46.99% in rural and non-rural, respectively). The lack of supplementary insurance coverage was the largest non-need contributor in both rural and non-rural area, with an additional contribution coming from education.
Interestingly, high SES made a very small contribution to the pro-rich inequalities in rural area while low SES was instead offsetting the inequalities in non-rural area.
Discussion
The results of this study suggest firstly, that whereas in- patient services are fairly equitable and seem to meet the principle of horizontal equity, the use of outpatient ser- vices is substantially concentrated among the well-off population. Second, that rural areas displayed lower levels of inequality in the use of inpatient services while non-rural areas showed lower levels of inequalities in the use of outpatient services. Third, the decomposition analysis suggested that non-need factors were the most Table 1 Variable characteristics
Characteristic Non-rural (%) Rural (%)
Sex
Female 25,904(49.32) 12,753(49.32)
Male 26,616(50.68) 13,105(50.68)
Education
Uneducated & Elementary 25,456(52.61) 17,985(77.20) Middle & High school 12,830(26.52) 3877(16.64)
College and above 10,099(20.87) 1435(6.16)
Socioeconomic Status (SES)
Poorest SES 10,818(21.06) 4773(19.59)
2th SES 9587(18.66) 4646(19.07)
Middle SES 9960(19.39) 4492(18.43)
4th SES 10,446(20.33) 4308(17.68)
5th SES 10,566(20.57) 6150(25.24)
Basic insurance
Yes 46,580(88.69) 24,469(94.63)
No 5940(11.31) 1389(5.37)
Supplementary Insurance
Yes 11,617(24.58) 1736(7.07)
No 35,645(75.42) 22,828(92.93)
Age
< 30 26,878(51.18) 14,215(54.97)
30–59 20,292(38.64) 8788(33.99)
≥ 60 5350(10.19) 2855(11.04)
Marital status
Single 18,455(40.54) 8601(39.85)
Married 27,068(59.46) 12,981(60.15)
being employed
Yes 27,163(65.60) 12,188(63.13)
No 14,241(34.40) 7118(36.87)
Health status
Good 46,690(88.90) 22,875(88.46)
Poor 5830(11.10) 2983(11.54)
Outpatient utilization
Yes 8743(16.65) 4441(17.17)
No 43,777(83.35) 21,417(82.83)
Inpatient utilization
Yes 3582(6.82) 1181(7.00)
No 48,938(93.18) 24,047(93.00)
Mostafaviet al. BMC Public Health (2020) 20:914 Page 4 of 9
important contributors to explain both inpatient and outpatient inequalities, and among them, the lack of supplementary insurance and SES were the most im- portant explanatory factors.
The overall observed pattern of inequality in out- patient and inpatient healthcare services in our study is in accordance with the findings of studies con- ducted in three high-income countries in East Asia (Honk Kong, South Korea, and Taiwan) [26] and Brazil [27]. On the one hand, private insurance cover- age (Hong Kong and Brazil), low education, un- employment (South Korea), place of residency and income (Taiwan) were the main explanatory factors for outpatient pro-rich inequalities. On the other hand, policy interventions as for example services-on- wheel and exemption of co-payment in rural residents were driving the pro-poor inpatients inequalities [26, 27]. Contrasting patterns to those found in the present study have also been described in other set- tings. For example a study in China reported pro-rich inequity of inpatient utilization in rural residents [28].
Our study adds to this meagre literature by suggest- ing the levels of inequalities in the use of inpatient services among rural residents is lower than in the non-rural ones. Possible explanations of our findings could be the successful implementation of the family physician program, the rural insurance scheme and the referral system, which have already shown in- creased equal and comfortable access to health ser- vices [11, 29]. As a result of these interventions, family physicians act as a gatekeeper to the system and rural insurance holders only pay a small portion of the total costs when they are admitted to hospitals
through the referral system. Conversely, in large cit- ies, family physicians doesn’t have an obvious role as gate-keepers [30].
This study also suggested that utilization of outpatient service is not equitable among Iranian population. In our study, the pro-rich inequality in outpatient services was higher in rural than in non-rural areas. This in- equality was mainly explained by the higher level of utilization among people with no supplementary health insurance, which was in fact concentrated among the high SES. In Iran, there are three type of health care ser- vices including; public sectors, quasi-public sectors and private sectors. While hospitalization services are mainly provided by the public sector (more than 70% of in- patient facilities and services), more than 70% of out- patient facilities and services are provided by the private sector [31]. Private sector fee is much higher than the public sector’s, which therefore could lead to pro-rich outpatient services. Despite increasing the share of gov- ernment and insurance funds in total health expendi- tures, finding has shown the out of pocket payments of the households increased even more than the previous years [15]. After the implementation of the Health trans- formation Plan, private sector services fee increased by an average of over 100% [32]. In addition, part of the outpatient service including dental services and rehabili- tation services are not covered by insurance and have also been reported to be pro-rich [31, 33]. Despite the high insurance coverage in Iran, service coverage and cost coverage by insurances are not sufficient when an individual need to use the private sector, which conse- quently has led to reduced access and utilization of ser- vices among the low-income individuals [34]. Also
Fig. 1 Concentration curve for outpatient and inpatient health care utilization in rural and non-rural
evidence of other studies in Iran have shown that low quality of health care delivery by public sectors and fam- ily physicians led to seeking care in private sectors and
specialist services particularly in outpatient care [35,36].
Similar to our findings, a previous study in Iran showed that poor socioeconomic status was associated with low Table 2 Decomposition of Concentration Index for Inpatient health care utilization in rural and non-rural
Rural Non-rural
Coef Elast CI Cont to C C% Coef Elast CI Cont to C C%
Age
< 30
30–59 0.006* 0.030 −0.032 −0.0009 1.23 −0.000 − 0.004 − 0.039 0.0001 − 0.17
≥ 60 0.014*** 0.022 0.012 0.0002 −0.34 0.005 0.007 0.016 0.0001 −0.13
Sex Female
Male 0.000 0.004 −0.007 −0.000 0.047 −0.003* − 0.029 − 0.008 0.0002 − 0.26
Health statue Good
Poor 0.71*** 1.180 −0.008 − 0.010 12.90 0.733*** 1.193 −0.005 −0.006 6.40
Subtotal need −0.010 13.83 −0.005 5.84
Education
College and above
Middle & High school 0.000 0.000 −0.028 −0.000 0.003 0.809 −0.002 − 0.025 0.000 − 0.05
Uneducated & Elementary 0.000 0.003 0.203 0.000 −0.009 −0.003 − 0.026 0.188 − 0.005 5.22 Basic insurance
Yes
No −0.007 − 0.005 0.007 −0.000 0.05 −0.015 − 0.025 0.029 − 0.000 0.76
Supplementary insurance Yes
No −0.007 − 0.095 0.333 −0.031 40.10 −0.005 − 0.064 0.258 − 0.016 17.22
Being employed Yes
No −0.000 −0.005 − 0.012 0.000 − 0.07 0.005** 0.029 −0.011 − 0.000 0.36
Marital status Single
Married −0.001 − 0.009 0.011 −0.000 0.13 0.005** 0.046 0.024 0.001 −1.16
Economic statue
Poorest SES 0.013** 0.044 −0.227 − 0.010 12.69 0.003 0.032 −0.540 −0.017 18.03
2th SES 0.170*** 0.050 −0.083 −0.004 5.38 −0.000 − 0.000 0.057 − 0.000 0.00
Middle SES 0.023*** 0.071 0.024 0.001 −2.16 0.004 0.006 0.068 0.000 −0.48
4th SES −0.002 − 0.007 0.098 − 0.000 0.91 0.000 0.000 0.765 0.000 −0.07
5th SES
Subtotal non-need −0.044 57.024 − 0.038 39.83
Total −0.099 −0.043
Residual 0.020 −0.053
C −0.079*** −0.096***
HI −0.068 −0.090***
Coeff Marginal effects, Elast elasticity, CI Concentration index of the social determinants, Cont to C Contribution to the overall concentration index, C% unadjusted percentage calculated on the overall explained portion of the C, HI Horizontal Index
* 0.01≤ p < 0.05; ** 0.001 ≤ p < 0.01; *** p < 0.001
Mostafaviet al. BMC Public Health (2020) 20:914 Page 6 of 9
utilization of services [37]. In contrast of our study and other studies in Iran, a local study conducted in 2012 in Shiraz (the fifth most populous city of Iran) reported a
pro-poor inequality in utilization of outpatient services after standardizing for need factors. The allocation of subsidies and low cost of services in the public sector, Table 3 Decomposition of Concentration Index for Outpatient health care utilization in rural and non-rural
Rural Non-rural
Coef Elast CI Cont to C C% Coef Elast CI Cont to C C%
Age
< 30
30–59 −0.014*** − 0.029 − 0.032 0.000 2.45 −0.000 − 0.001 − 0.039 0.000 0.63
≥ 60 −0.015*** − 0.009 0.012 −0.000 − 0.30 − 0.004 − 0.003 0.016 − 0.000 − 0.70
Sex Female
Male −0.007* −0.022 −0.007 0.000 0.45 0.001 0.003 −0.008 −0.000 − 0.46
Health statue Good
Poor 0.320*** 0.215 −0.008 −0.001 −4.86 0.328*** 0.219 −0.005 −0.001 −15.77
Subtotal need −0.000 −2.26 −0.001 −16.30
Education
College and above
Middle & High school −0.006 − 0.006 − 0.028 0.000 0.45 0.002 0.004 −0.025 −0.000 −1.45
Uneducated & Elementary 0.007 0.032 0.203 0.006 17.05 0.004 0.013 0.188 0.002 35.15
Basic insurance Yes
No −0.039*** −0.012 0.007 −0.000 −0.25 0.000 0.000 0.029 0.000 0.25
Supplementary insurance Yes
No 0.015** 0.085 0.333 0.028 74.05 0.009*** 0.040 0.258 0.010 147.63
Being employed Yes
No 0.016*** 0.035 −0.012 − 0.000 −1.14 − 0.003 − 0.007 −0.011 0.000 1.28
Martial statue Single
Married −0.005** −0.019 0.011 −0.000 − 0.60 − 0.008** − 0.029 0.024 − 0.000 −9.89
Economic statue
Poorest SES 0.001 0.001 −0.227 − 0.000 − 0.96 0.005 0.019 − 0.540 − 0.010 − 149.42
2th SES − 0.001 − 0.002 − 0.083 0.000 0.51 0.009 0.009 0.057 0.000 7.93
Middle SES −0.013** −0.016 0.024 −0.000 −1.02 0.004 0.002 0.068 0.000 2.61
4th SES 0.018*** 0.018 0.098 0.001 4.87 0.021*** 0.012 0.765 0.000 12.90
5th SES
Subtotal non-need 0.035 92.96 0.003 46.99
Total 0.0358 0.002
Residual 0.002 0.005
C 0.038* 0.007
HI 0.039*** 0.008***
Coeff Marginal effects, Elast elasticity, CI Concentration index of the social determinants, Cont to C Contribution to the overall concentration index, C% unadjusted percentage calculated on the overall explained portion of the C, HI Horizontal Index
* 0.01≤ p < 0.05; ** 0.001 ≤ p < 0.01; *** p < 0.001
high insurance coverage, and financial barriers related to upper level access, reported as the main factors associ- ated to the pro-poor inequality; in addition the reason reported for under-utilization of the rich individuals is low quality of services in Shiraz [38].
This study has some limitations; the data on socioeco- nomic status, health status, and the utilization of services were collected via self-reported questionnaire, as a re- sult, the collected data might have some bias. In the present study a PCA analysis was used to calculate so- cioeconomic variable therefore the choice of variables and the appropriateness of the weights assigned to them might be amatter of concern [4]. In addition, the self- reported health variable was binary (good and poor health), which will not indicate variation in health status and might not adequately discriminate those in need for health care services, therefore its effect may be overesti- mated or underestimated. Another limitation is big residuals in some of the models meaning the variables included in the model were not able to adequately explain the inequalities in the outcome. Some other variables i.e. quality of health care delivery could have been relevant to explain these inequalities, however information was not available in the dataset. Also to handle the limitation of secondary data, survey method including the survey instrument was considered. Con- cerning the analysis, we used the correction proposed by Wagstaff et al. to the concentration index because of the binary nature of the health outcome [4].
Conclusions
Our results, suggest a pro-poor income-related inequal- ity in inpatient and pro-rich income-related inequality in outpatient services. Inequalities are mainly explained by non-need factors i.e. lack of supplementary insurance and SES. Magnitude of HI was greater in rural areas for outpatient services and greater in non-rural area for in- patient services. Disentangle the different contribution of determinants as well as variations in HI among rural and non-rural areas provide helpful information for de- cision makers to re-design policy and re-distribute re- source allocation in order to reduce the socioeconomic gradient in health care utilization.
Abbreviations
C:Concentration Index; CC: Concentration Curve; HI: Horizontal Inequity index; UHC: Universal health coverage; PHC: Public health care;
UHS: Utilization of Health Services; PCA: Principal component analysis
Acknowledgements
This project was supported and registered by Kurdistan University Medical Science in Iran.
Authors’ contributions
FM and GM contributed to the conceptualizing, planning and writing the article, statistical analysis, result interpretation, literature review. PM, contributed to the result interpretation, literature review. BP and RM
contributed to literature review. All authors read and approved the final manuscript.
Funding
The authors received no specific funding for publishing, or preparation of the manuscript of this work.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Kurdistan University of Medical Sciences Ethics Committee (with IR.MUK.REC.1395/396 the committee’s reference number).
Consent for publication Not applicable.
Competing interests
The authors declare that they have no conflict of interest.
Author details
1Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran.2Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, Umeå, Sweden.
3School of Public Health and Institute of Public Health Research,
Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran.4Department of Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Pasdaran Ave, Sanandaj, Iran.
Received: 7 October 2019 Accepted: 8 June 2020
References
1. Boerma T, Eozenou P, Evans D, Evans T, Kieny M-P, Wagstaff A. Monitoring progress towards universal health coverage at country and global levels.
PLoS Med. 2014;11(9):e1001731.
2. Carrin G, James C, Organization WH. Reaching universal coverage via social health insurance: key design features in the transition period; 2004.
3. Atun R, Aydın S, Chakraborty S, Sümer S, Aran M, Gürol I, et al. Universal health coverage in Turkey: enhancement of equity. Lancet. 2013;382(9886):
65–99.
4. O’Donnell OA, Wagstaff A. Analyzing health equity using household survey data: a guide to techniques and their implementation: World Bank publications; 2008.
5. Culyer AJ, Wagstaff A. Equity and equality in health and health care. J Health Econ. 1993;12(4):431–57.
6. Ev D, Koolman X, Jones AM. Explaining income†related inequalities in doctor utilisation in Europe. Health Econ. 2004;13(7):629–47.
7. Fendall N. Declaration of Alma-Ata. Lancet. 1978;312(8103):1308.
8. Bonilla-Chacín ME, Aguilera N. The Mexican social protection system in health; 2013.
9. Takian A, Rashidian A, Kabir MJ. Expediency and coincidence in re- engineering a health system: an interpretive approach to formation of family medicine in Iran. Health Policy Plan. 2010;26(2):163–73.
10. Ibrahimipour H, Maleki M-R, Brown R, Gohari M, Karimi I, Dehnavieh R. A qualitative study of the difficulties in reaching sustainable universal health insurance coverage in Iran. Health Policy Plan. 2011;26(6):485–95.
11. Kalhor R, Azmal M, Kiaei MZ, Eslamian M, Tabatabaee SS, Jafari M. Situational analysis of human resources in family physician program: survey from Iran.
Materia socio-Med. 2014;26(3):195.
12. Takian A, Rashidian A, Doshmangir L. The experience of purchaser–provider split in the implementation of family physician and rural health insurance in Iran: an institutional approach. Health Policy Plan. 2015;30(10):1261–71.
13. Takian A, Doshmangir L, Rashidian A. Implementing family physician programme in rural Iran: exploring the role of an existing primary health care network. Fam Pract. 2013;30(5):551–9.
14. Doshmangir L, Rashidian A, Ravaghi H, Takian A, Jafari M. The experience of implementing the board of trustees’ policy in teaching hospitals in Iran: an
Mostafaviet al. BMC Public Health (2020) 20:914 Page 8 of 9
example of health system decentralization. Int J Health Policy Manag.
2015;4(4):207.
15. Yazdi-Feyzabadi V, Bahrampour M, Rashidian A, Haghdoost A-A, Javar MA, Mehrolhassani MH. Prevalence and intensity of catastrophic health care expenditures in Iran from 2008 to 2015: a study on Iranian household income and expenditure survey. Int J Equity Health. 2018;17(1):44.
16. Ehsani-Chimeh E, Sajadi HS, Majdzadeh R. Iran towards universal health coverage: the role of human resources for health. Med J Islamic Repub Iran (MJIRI). 2018;32(1):578–82.
17. Mohammadbeigi A, Hassanzadeh J, Eshrati B, Rezaianzadeh A.
Decomposition of inequity determinants of healthcare utilization, Iran.
Public Health. 2013;127(7):661–7.
18. Pourreza A, Khabiri R, Rahimi Foroushani A, Akbari Sari A, Arab M, Kavosi Z.
Health care-seeking behavior in Tehran, Islamic Republic of Iran. World Appl Sci J. 2011;14(8):1190–7.
19. Etemad K, Yavari P, Mehrabi Y, Haghdoost A, Motlagh ME, Kabir MJ, et al.
Inequality in utilization of in-patients health services in Iran. Int J Prev Med.
2015;6:45.
20. Abouie A, Majdzadeh R, Khabiri R, Hamedi-Shahraki S, Emami Razavi SH, Yekaninejad MS. Socioeconomic inequities in health services’ utilization following the health transformation plan initiative in Iran. Health Policy Plan.
2018;33(10):1065.
21. Moradi G, Mostafavi F, Azadi N, Esmaeilnasab N, Ghaderi E. Socioeconomic inequality in childhood obesity. J Res Health Sci. 2017;17:3.
22. Wagstaff A, O’Donnell O, Van Doorslaer E, Lindelow M. Analyzing health equity using household survey data: a guide to techniques and their implementation: World Bank publications; 2007.
23. Wagstaff A. The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Econ. 2005;14(4):429–32.
24. Mosquera PA, Waenerlund A-K, Goicolea I, Gustafsson PE. Equitable health services for the young? A decomposition of income-related inequalities in young adults’ utilization of health care in northern Sweden. Int J Equity Health. 2017;16(1):20.
25. Van Doorslaer E, Wagstaff A, Van der Burg H, Christiansen T, De Graeve D, Duchesne I, et al. Equity in the delivery of health care in Europe and the US.
J Health Econ. 2000;19(5):553–83.
26. Jui-fen RL, Leung GM, Kwon S, Tin KY, Van Doorslaer E, O’Donnell O.
Horizontal equity in health care utilization evidence from three high-income Asian economies. Soc Sci Med. 2007;64(1):199–212.
27. Macinko J, Lima-Costa MF. Horizontal equity in health care utilization in Brazil, 1998–2008. Int J Equity Health. 2012;11(1):33.
28. Zhou Z, Gao J, Fox A, Rao K, Xu K, Xu L, et al. Measuring the equity of inpatient utilization in Chinese rural areas. BMC Health Serv Res.
2011;11(1):201.
29. Khayyati F, Motlagh ME, Kabir M, Kazemeini H. The role of family physician in case finding, referral, and insurance coverage in the rural areas. Iran J Public Health. 2011;40(3):136.
30. Takian A. Implementing family medicine in Iran identification of facilitators and obstacles: London School of Hygiene & tropical medicine; 2009.
31. Rezaei S, Hajizadeh M, Bazyar M, Kazemi Karyani A, Jahani B, Karami MB. The impact of health sector evolution plan on the performance of hospitals in Iran: evidence from the Pabon lasso model. Int J Health Governance. 2018;
23(2):111–9.
32. Moghadam TZ, Raeissi P, Jafari-Sirizi M. Analysis of the health sector evolution plan from the perspective of equity in healthcare financing: a multiple streams model. Int J Human Rights Healthc. 2018.https://doi.org/
10.1108/IJHRH-07-2018-0044.
33. Karami Matin B, Hajizadeh M, Najafi F, Homaie Rad E, Piroozi B, Rezaei S. The impact of health sector evolution plan on hospitalization and cesarean section rates in Iran: an interrupted time series analysis. Int J Qual Health Care. 2017;30(1):75–9.
34. Rashidian A, Joudaki H, Khodayari-Moez E, Omranikhoo H, Geraili B, Arab M.
The impact of rural health system reform on hospitalization rates in the Islamic Republic of Iran: an interrupted time series. Bull World Health Organ.
2013;91:942–9.
35. Dehnavieh R, Kalantari AR, Sirizi MJ. Urban family physician plan in Iran:
challenges of implementation in Kerman. Med J Islam Repub Iran.
2015;29:303.
36. LeBaron SW, Schultz SH. Family medicine in Iran: the birth of a new specialty. Fam Med-Kansas City. 2005;37(7):502.
37. Piroozi B, Moradi G, Nouri B, Bolbanabad AM, Safari H. Catastrophic health expenditure after the implementation of health sector evolution plan: a case study in the west of Iran. Int J Health Policy Manag. 2016;5(7):417.
38. Kavosi Z, Mohammadbeigi A, Ramezani-Doroh V, Hatam N, Jafari A, Firoozjahantighi A. Horizontal inequity in access to outpatient services among Shiraz City residents, Iran. J Res Health Sci. 2015;15(1):37–41.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
